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A TFX component to validate input examples.
Inherits From: BaseComponent
, BaseNode
tfx.components.ExampleValidator(
statistics: tfx.types.Channel
= None,
schema: tfx.types.Channel
= None,
exclude_splits: Optional[List[Text]] = None,
output: Optional[tfx.types.Channel
] = None,
stats: Optional[tfx.types.Channel
] = None,
instance_name: Optional[Text] = None
)
Used in the notebooks
Used in the tutorials |
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The ExampleValidator component uses Tensorflow Data Validation to validate the statistics of some splits on input examples against a schema.
The ExampleValidator component identifies anomalies in training and serving data. The component can be configured to detect different classes of anomalies in the data. It can:
- perform validity checks by comparing data statistics against a schema that codifies expectations of the user.
Schema Based Example Validation The ExampleValidator component identifies any anomalies in the example data by comparing data statistics computed by the StatisticsGen component against a schema. The schema codifies properties which the input data is expected to satisfy, and is provided and maintained by the user.
Please see https://www.tensorflow.org/tfx/data_validation for more details.
Example
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
Args | |
---|---|
statistics
|
A Channel of type standard_artifacts.ExampleStatistics .
|
schema
|
A Channel of type standard_artifacts.Schema . required
|
exclude_splits
|
Names of splits that the example validator should not validate. Default behavior (when exclude_splits is set to None) is excluding no splits. |
output
|
Output channel of type standard_artifacts.ExampleAnomalies .
|
stats
|
Backwards compatibility alias for the 'statistics' argument. |
instance_name
|
Optional name assigned to this specific instance of
ExampleValidator. Required only if multiple ExampleValidator components
are declared in the same pipeline. Either stats or statistics must
be present in the arguments.
|
Attributes | |
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component_id
|
|
component_type
|
|
downstream_nodes
|
|
exec_properties
|
|
id
|
Node id, unique across all TFX nodes in a pipeline.
If |
inputs
|
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outputs
|
|
type
|
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upstream_nodes
|
Child Classes
Methods
add_downstream_node
add_downstream_node(
downstream_node
)
Experimental: Add another component that must run after this one.
This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.
Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.
It is symmetric with add_upstream_node
.
Args | |
---|---|
downstream_node
|
a component that must run after this node. |
add_upstream_node
add_upstream_node(
upstream_node
)
Experimental: Add another component that must run before this one.
This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.
Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.
It is symmetric with add_downstream_node
.
Args | |
---|---|
upstream_node
|
a component that must run before this node. |
from_json_dict
@classmethod
from_json_dict( dict_data: Dict[Text, Any] ) -> Any
Convert from dictionary data to an object.
get_id
@classmethod
get_id( instance_name: Optional[Text] = None )
Gets the id of a node.
This can be used during pipeline authoring time. For example: from tfx.components import Trainer
resolver = ResolverNode(..., model=Channel( type=Model, producer_component_id=Trainer.get_id('my_trainer')))
Args | |
---|---|
instance_name
|
(Optional) instance name of a node. If given, the instance name will be taken into consideration when generating the id. |
Returns | |
---|---|
an id for the node. |
to_json_dict
to_json_dict() -> Dict[Text, Any]
Convert from an object to a JSON serializable dictionary.
with_id
with_id(
id: Text
) -> "BaseNode"
with_platform_config
with_platform_config(
config: message.Message
) -> "BaseComponent"
Attaches a proto-form platform config to a component.
The config will be a per-node platform-specific config.
Args | |
---|---|
config
|
platform config to attach to the component. |
Returns | |
---|---|
the same component itself. |
Class Variables | |
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EXECUTOR_SPEC |
tfx.dsl.components.base.executor_spec.ExecutorClassSpec
|